IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第2页

IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information IEEE计算智能新兴主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-26 DOI: 10.1109/TETCI.2025.3548330

引用次数: 0

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-26 DOI: 10.1109/TETCI.2025.3548332

引用次数: 0

Leveraging Pixel Difference Feature for Deepfake Detection 利用像素差分特征进行深度伪造检测

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-21 DOI: 10.1109/TETCI.2025.3548803

Maoyu Mao;Chungang Yan;Junli Wang;Jun Yang

{"title":"Leveraging Pixel Difference Feature for Deepfake Detection","authors":"Maoyu Mao;Chungang Yan;Junli Wang;Jun Yang","doi":"10.1109/TETCI.2025.3548803","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3548803","url":null,"abstract":"The rise of Deepfake technology poses a formidable threat to the credibility of both judicial evidence and intellectual property safeguards. Current methods lack the ability to integrate the texture information of facial features into CNNs, despite the fact that fake contents are subtle and pixel-level. Due to the fixed grid kernel structure, CNNs are limited in their ability to describe detailed fine-grained information, making it challenging to achieve accurate image detection through pixel-level fine-grained features. To mitigate this problem, we propose a Pixel Difference Convolution (PDC) to capture local intrinsic detailed patterns via aggregating both intensity and gradient information. To avoid the redundant feature computations generated by PDC and explicitly enhance the representational power of a standard convolutional kernel, we separate PDC into vertical/horizontal and diagonal parts. Furthermore, we propose an Ensemble Dilated Convolution (EDC) to explore long-range contextual dependencies and further boost performance. We introduce a novel network, Pixel Difference Convolutional Network (PDCNet), which is built with PDC and EDC to expose Deepfake by capturing faint traces of tampering hidden in portrait images. By leveraging PDC and EDC in the information propagation process, PDCNet seamlessly incorporates both local and global pixel differences. Comprehensive experiments are performed on three databases, FF++, Celeb-DF, and DFDC to confirm that our PDCNet outperforms existing approaches. Our approach achieves accuracies of 0.9634, 0.9614, and 0.8819 in FF++, Celeb-DF, and DFDC, respectively.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3178-3188"},"PeriodicalIF":5.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification 一种微扰一致性驱动的医学图像分割与分类主动学习方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-18 DOI: 10.1109/TETCI.2025.3547635

Tao Wang;Xinlin Zhang;Yuanbo Zhou;Yuanbin Chen;Longxuan Zhao;Tao Tan;Tong Tong

{"title":"PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification","authors":"Tao Wang;Xinlin Zhang;Yuanbo Zhou;Yuanbin Chen;Longxuan Zhao;Tao Tan;Tong Tong","doi":"10.1109/TETCI.2025.3547635","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3547635","url":null,"abstract":"In recent years, supervised learning using convolutional neural networks (CNN) has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Moreover, effective utilization of annotation resources might not always be feasible during the annotation process. To optimize the utilization of annotation resources, a proposed active learning framework is introduced that is applicable to both 2D and 3D segmentation and classification tasks. This framework aims to reduce annotation costs by selecting more valuable samples for annotation from the pool of unlabeled data. Based on the perturbation consistency, we apply different perturbations to the input data and propose a perturbation consistency evaluation module to evaluate the consistency among predictions when applying different perturbations to the data. Subsequently, we rank the consistency of each data and select samples with lower consistency as high-value candidates. These selected samples are prioritized for annotation. We extensively validated our proposed framework on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our proposed framework can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The proposed framework enables more efficient utilization of annotation resources by annotating more representative samples, thus enhancing the model's robustness with fewer annotation costs.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3162-3177"},"PeriodicalIF":5.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PurifyFL: Non-Interactive Privacy-Preserving Federated Learning Against Poisoning Attacks Based on Single Server PurifyFL：针对单服务器中毒攻击的非交互式隐私保护联邦学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-17 DOI: 10.1109/TETCI.2025.3540420

Yanli Ren;Zhe Yang;Guorui Feng;Xinpeng Zhang

引用次数: 0

A Learning-Based Two-Stage Multi-Thread Iterated Greedy Algorithm for Co-Scheduling of Distributed Factories and Automated Guided Vehicles With Sequence-Dependent Setup Times 一种基于学习的两阶段多线程迭代贪心算法用于序列依赖的分布式工厂和自动导引车协同调度

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-14 DOI: 10.1109/TETCI.2025.3540405

Zijiang Liu;Hongyan Sang;Biao Zhang;Leilei Meng;Tao Meng

{"title":"A Learning-Based Two-Stage Multi-Thread Iterated Greedy Algorithm for Co-Scheduling of Distributed Factories and Automated Guided Vehicles With Sequence-Dependent Setup Times","authors":"Zijiang Liu;Hongyan Sang;Biao Zhang;Leilei Meng;Tao Meng","doi":"10.1109/TETCI.2025.3540405","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3540405","url":null,"abstract":"Automated guided vehicles are widely utilized in the real production environment for tasks such as job transfer and inter-factory collaboration, yet they remain relatively underexplored in academic research. This study addresses the distributed permutation flow shop co-scheduling problem with sequence-dependent setup times (DPFCSP-SDST). We propose a novel solution that leverages an optimization algorithm, specifically a learning-based two-stage multi-thread iterated greedy algorithm (LTMIG). First, a problem-specific initialization method is designed to generate the initialization solution in two stages. Second, a Q-learning-based operator adaptation strategy is adopted to guide the evolutionary direction of factory assignment to reduce the makespan. Then, the proposed destructive-construction strategy builds an archive set to share historical knowledge with different stages of search, ensuring exploration capability. Local search effectively combines the parallel computing power of multi-threading with the inherent exploitation capability of LTMIG, and fully utilizes the information of elite solutions. Extensive experimental results demonstrate that LTMIG is significantly better than the comparison algorithms mentioned in the paper, and it turns out that LTMIG is the most suitable algorithm for solving DPFCSP-SDST.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2208-2218"},"PeriodicalIF":5.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HGRL-S: Towards Heterogeneous Graph Representation Learning With Optimized Structures HGRL-S：面向优化结构的异构图表示学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-14 DOI: 10.1109/TETCI.2025.3543414

Shanfeng Wang;Dong Wang;Xiaona Ruan;Xiaolong Fan;Maoguo Gong;He Zhang

{"title":"HGRL-S: Towards Heterogeneous Graph Representation Learning With Optimized Structures","authors":"Shanfeng Wang;Dong Wang;Xiaona Ruan;Xiaolong Fan;Maoguo Gong;He Zhang","doi":"10.1109/TETCI.2025.3543414","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3543414","url":null,"abstract":"Heterogeneous Graph Neural Networks (HetGNN) have garnered significant attention and demonstrated success in tackling various tasks. However, most existing HetGNNs face challenges in effectively addressing unreliable heterogeneous graph structures and encounter semantic indistinguishability problems as their depth increases. In an effort to deal with these challenges, we introduce a novel heterogeneous graph representation learning with optimized structures to optimize heterogeneous graph structures and utilize semantic aggregation mechanism to alleviate semantic indistinguishability while learning node embeddings. To address the heterogeneity of relations within heterogeneous graphs, the proposed algorithm employs a strategy of generating distinct relational subgraphs and incorporating them with node features to optimize structural learning. To resolve the issue of semantic indistinguishability, the proposed algorithm adopts a semantic aggregation mechanism to assign appropriate weights to different meta-paths, consequently enhancing the effectiveness of captured node features. This methodology enables the learning of distinguishable node embeddings by a deeper HetGNN model. Extensive experiments on the node classification task validate the promising performance of the proposed framework when compared with state-of-the-art methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2359-2370"},"PeriodicalIF":5.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Anxiety via Machine Learning Algorithms: A Literature Review 通过机器学习算法检测焦虑：文献综述

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-12 DOI: 10.1109/TETCI.2025.3543307

M.-H. Tayarani-N.;Shamim Ibne Shahid

{"title":"Detecting Anxiety via Machine Learning Algorithms: A Literature Review","authors":"M.-H. Tayarani-N.;Shamim Ibne Shahid","doi":"10.1109/TETCI.2025.3543307","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3543307","url":null,"abstract":"Recent machine learning (ML) advances have opened up new possibilities for addressing various challenges. Given their ability to tackle complex problems, the use of ML algorithms in diagnosing mental health disorders has seen substantial growth in both the number and scope of studies. Anxiety, a major health concern in today's world, affects a significant portion of the population. Individuals with anxiety often exhibit distinct characteristics compared to those without the disorder. These differences can be observed in their outward appearance—such as voice, facial expressions, gestures, and movements—and in less visible factors like heart rate, blood test results, and brain imaging data. In this context, numerous studies have utilized ML algorithms to extract a diverse range of features from individuals with anxiety, aiming to build predictive models capable of accurately identifying those affected by the disorder. This paper performs a comprehensive literature review on the state-of-the-art studies that employ machine learning algorithms to identify anxiety. This paper aims to cover a wide range of studies and categorize them based on their methodologies and data types used.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"2634-2657"},"PeriodicalIF":5.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CVRSF-Net: Image Emotion Recognition by Combining Visual Relationship Features and Scene Features CVRSF-Net：结合视觉关系特征和场景特征的图像情感识别

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-10 DOI: 10.1109/TETCI.2025.3543300

Yutong Luo;Xinyue Zhong;Jialan Xie;Guangyuan Liu

{"title":"CVRSF-Net: Image Emotion Recognition by Combining Visual Relationship Features and Scene Features","authors":"Yutong Luo;Xinyue Zhong;Jialan Xie;Guangyuan Liu","doi":"10.1109/TETCI.2025.3543300","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3543300","url":null,"abstract":"Image emotion recognition, which aims to analyze the emotional responses of people to various stimuli in images, has attracted substantial attention in recent years with the proliferation of social media. As human emotion is a highly complex and abstract cognitive process, simply extracting local or global features from an image is not sufficient for recognizing the emotion of an image. The psychologist Moshe proposed that visual objects are usually embedded in a scene with other related objects during human visual comprehension of images. Therefore, we propose a two-branch emotion-recognition network known as the combined visual relationship feature and scene feature network (CVRSF-Net). In the scene feature-extraction branch, a pretrained CLIP model is adopted to extract the visual features of images, with a feature channel weighting module to extract the scene features. In the visual relationship feature-extraction branch, a visual relationship detection model is used to extract the visual relationships in the images, and a semantic fusion module fuses the scenes and visual relationship features. Furthermore, we spatially weight the visual relationship features using class activation maps. Finally, the implicit relationships between different visual relationship features are obtained using a graph attention network, and a two-branch network loss function is designed to train the model. The experimental results showed that the recognition rates of the proposed network were 79.80%, 69.81%, and 36.72% for the FI-8, Emotion-6, and WEBEmo datasets, respectively. The proposed algorithm achieves state-of-the-art results compared to existing methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2321-2333"},"PeriodicalIF":5.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GAMR: Revolutionizing Multi-Objective Routing in SDN Networks With Dynamic Genetic Algorithms 用动态遗传算法革新SDN网络中的多目标路由

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-10 DOI: 10.1109/TETCI.2025.3543836

Hai-Anh Tran;Cong-Son Duong;Trong-Duc Bui;Van Tong;Huynh Thi Thanh Binh

{"title":"GAMR: Revolutionizing Multi-Objective Routing in SDN Networks With Dynamic Genetic Algorithms","authors":"Hai-Anh Tran;Cong-Son Duong;Trong-Duc Bui;Van Tong;Huynh Thi Thanh Binh","doi":"10.1109/TETCI.2025.3543836","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3543836","url":null,"abstract":"The growing complexity of modern network systems has increased the need for efficient multi-objective routing (MOR) algorithms. However, existing MOR methods face significant challenges, particularly in terms of computation time, which becomes problematic in networks with short-lived tasks where rapid decision-making is essential. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) offers a promising approach to addressing these challenges. Nevertheless, directly applying NSGA-II in dynamic network environments, where states frequently change, is impractical. This paper presents GAMR, an enhanced non-dominated sorting Genetic Algorithm II-based dynamic multi-objective QoS routing algorithm, which leverages QoS metrics for its multi-objective function. Introducing novel initialization and crossover strategies, our approach efficiently identifies optimal solutions within a brief runtime. Implemented within a Software-defined Network controller for routing, GAMR outperforms existing multi-objective algorithms, exhibiting notable improvements in performance indicators. Specifically, enhancements range from 3.4% to 22.8% on the Hypervolume metric and from 33% to 86% on the Inverted Generational Distance metric. In terms of network metrics, experimental results demonstrate significant reductions in forwarding delay and packet loss rate to 41.25 ms and 3.9%, respectively, even under challenging network configurations with only 2 servers and up to 100 requests.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3147-3161"},"PeriodicalIF":5.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0